Many companies today store vast amounts of data in online transaction processing (OLTP) systems and other databases. For example, the data may include business data such as sales, product, and financial data. Decision-makers frequently rely on such data in making business decisions.
However, unlike OLTP systems, which typically capture transaction data for a business, online analytical processing (OLAP) systems summarize the transaction data to further inform decision-making. For example, a business analyst may interpret data aggregated across various business dimensions in an OLAP system. The business analyst may browse, in various contexts, data from the OLAP system. For instance, the business analyst may view sales by product by customer by time, defects by manufacturing plant by time, etc.
Generally, OLAP allows multidimensional analysis of data. That is, OLAP provides data in a form of “views” or “dimensions” that are organized to reflect the multidimensional nature of the data. An OLAP system typically includes data models that allow business analysts to interactively explore data across multiple viewpoints at multiple levels of aggregation, also referred to as levels. An increasingly popular conceptual model for OLAP systems is a data cube (or simply, cube). An OLAP system may store a number of cubes. Each cube includes a set of dimensions (e.g., Time, Geography, Product, etc.). A dimension typically comprises many levels, and the levels are typically hierarchical (e.g., Month, Quarter, and Year for the Time dimension; City, Province, and Country for the Geography dimension, etc.).